People live in an organized society. In today's world where technology advances rapidly and the structure of human society becomes more complicated, the organization of the society becomes more conscientious. People with different identities are given specific rights to conduct corresponding duties. For example, people with different identities are given rights to access different places, such as one is not allowed to trespass other people's houses or administrators in a company are not allowed to enter the research center thereof.
In view of the above, various entrance security systems are developed, including recognition systems and security systems. Recognition systems are for example swipe card, password or biological features recognition systems or the like. Biological features identification systems typically include face recognition, pupil recognition, fingerprint recognition, or voiceprint recognition for recognizing people.
Generally, in the field of biological recognition, face recognition is the one most commonly used among all. However, the majority of current face recognition methods employ two-dimensional images for face recognition. Three-dimensional (3-D) face recognition is still under development. In current face recognition method, the approach is to select facial features as 3-D feature points and compare them with 3-D face models in a database. However, information on a human face varies greatly, often very different 3-D feature attributes are found at two neighboring locations. Thus, the accuracy of selecting facial feature points significantly affects the recognition result. Poorly selected feature points may result in exceedingly large errors between the 3-D information of the selected feature points and the accurate points.
Therefore, how to accurately select feature points for recognition to reduce errors in recognition is a problem urgently waiting to be solved.